Seasonal and diurnal variations in SAR signatures of landfast sea ice

Barber, David G.; LeDrew, E.; Flett, D.; Shokr, Mohammed; Falkingham, J.

doi:10.1109/36.142948

Cited by 26 publications

(11 citation statements)

References 4 publications

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“…In April/May, the snow thickness in the CAA ranges between 0.2 m and 0.4 m (Brown and Cote 1992;Melling 2002;Haas and Howell 2015). The immobile nature of the landfast ice in this region is ideal for studying sea ice evolution because there is negligible ice drift between acquisition times of the SAR imagery in the winter and melting conditions (Barber et al, 1992;Nasonova et al 2017).…”

Section: Study Areamentioning

confidence: 99%

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

Cafarella

Scharien

Geldsetzer

et al. 2019

Canadian Journal of Remote Sensing

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Sea ice surface roughness is a geophysical property which can be defined and quantified on a variety scales, and consequently affects processes across various scales. The sea ice surface roughness influences various mass, gas, and energy fluxes across the ocean-sea ice-atmosphere interface. Utilizing synthetic aperture radar (SAR) data to understand and map sea ice roughness is an active area of research. This thesis provides new techniques for the estimation of sea ice surface roughness in the Canadian Arctic Archipelago using synthetic aperture radar (SAR). Estimating and isolating sea ice surface properties from SAR imagery is complicated as there are a number of sea ice and sensor properties that influence the backscattered energy. There is increased difficulty in the melting season due to the presence of melt ponds on the surface, which can often inhibit interactions from the sensor to the sea ice surface as shorter microwaves cannot penetrate through the melt water. An object-based image analysis is here used to quantitatively link the winter first-year sea ice surface roughness to C-band RADARSAT-2 and L-band ALOS-2 PALSAR-2 SAR backscatter measured at two periods: winter (pre-melt) and advanced melt. Since the sea ice in our study area, the Canadian Arctic Archipelago, is landfast, the same ice can be imaged using SAR after the surface roughness measurements are established. Strong correlations between winter measured surface roughness, and C-and L-band SAR backscatter acquired during both the winter and advanced melt periods are observed. Results for winter indicate: (1) C-band HH-polarization backscatter is correlated with roughness (r=0.86) at a shallow incidence angle; and (2) L-band HH-and VV-polarization backscatter is correlated with roughness (r=0.82) at a moderate incidence angle. Results for advanced melt indicate: (1) C-band HV/HH polarization ratio is correlated with roughness (r=-0.83) at shallow incidence angle;(2) C-band HHpolarization backscatter is correlated with roughness (r=0.84) at shallow incidence angle for deformed first-year ice only; and (3) L-band HH-polarization backscatter is correlated with roughness (r=0.79) at moderate incidence angle. Retrieval models for surface roughness are developed and applied to the imagery to demonstrate the utility of SAR for mapping roughness, also as a proxy for deformation state, with a best case RMSE of 5 mm in the winter, and 8 mm during the advanced melt. iv

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Section: Study Areamentioning

confidence: 99%

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

Cafarella

Scharien

Geldsetzer

et al. 2019

Canadian Journal of Remote Sensing

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“…Whenever the relatively warm ocean is exposed directly to the cold winds blowing over the ice surface, the very strong temperature contrasts establish a massive energy flux. The open water areas thus act as 'thermal volcanoes' (LeDrew, 1992a). Figure 2 shows that even at the time of maximum ice extent there are large portions of the polar ice pack which have ice concentrations of less than 100%.…”

Section: Sea-ice Concentration/open Water Areamentioning

confidence: 98%

“…It is not without its limitations, however. While SAR data are quite powerful for the discrimination of various ice types under ideal conditions (i.e., in the middle of winter), the energy-matter interactions in a melting snow/ice surface are still poorly understood (Barber et al, 1992a). In addition, spacebome SARs (for example ERS-1, JERS-1, RADARSAT and EOS) have (or will have) relatively narrow swath widths (in the range of 100 km) which are unsuitable for hemispheric climate analyses.…”

Section: Microwave Sensorsmentioning

confidence: 99%

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Hypertemporal analysis of remotely sensed sea-ice data for climate change studies

Piwowar

LeDrew

1995

Progress in Physical Geography: Earth and Environment

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Climatologists have speculated that a spatially coherent pattern of high-latitude temperature trends could be an early indicator of climatic change. The sensitivity of sea ice to the temperature of the overlying air suggests the possibility that trends in Arctic ice conditions may be useful proxy indicators of general climatic changes. Aspects of the north-polar ice pack which have been identified as key parameters to be monitored include ice extent, concentration, type, thickness and motion dynamics. In spite of the considerable interannual, regional and seasonal variations exhibited by these data, there may be some evidence of an emerging trend towards decreasing ice extent and concentration. Collecting data in such a remote and harsh environment to support these analyses is only possible through satellite remote sensing. Remote sensing in the microwave portion of the electromagnetic spectrum is particularly relevant for polar applications because microwaves are capable of penetrating the atmosphere under virtually all conditions and are not dependent on the sun as a source of illumination. In particular, analyses of passive microwave imagery can provide us with daily information on sea-ice extent, type, concentration, dynamics and melt onset. A historical record of Arctic imagery from orbiting passive microwave sensors starting from 1973 provides us with an excellent data source for climate change studies. The development of analysis tools to support large area monitoring is integral to advancing global change research. The critical need is to create techniques which highlight the space-time relationships in the data rather than simply displaying voluminous quantities of data. In particular, hypertemporal image analysis techniques are required to help find anticipated trends and to discover unexpected or anomalous temporal relationships. Direct hypertemporal classification, principal components analysis and spatial time-series analysis are identified as three primary techniques for enhancing change in temporal image sequences. There is still a need for the development of new tools for spatialtemporal modelling.The earth's polar regions play a critical role in global change scenarios. Polar climates exist through a delicate balance between the limited amount of radiant energy received from at MOUNT ALLISON UNIV on June 13, 2015 ppg.sagepub.com Downloaded from 217 solar radiation and general atmospheric and oceanic circulation, and the high proportion of that heat which is reflected back into space or otherwise tied up within changes of phase of surface moisture. The net result is a permanent presence of ice and snow. In areal extent, sea ice accounts for nearly two-thirds of the earth's ice cover. In the north polar region, it spans an area ranging from a summer minimum of 8.5 X 106 km2 to almost twice that extent in winter.

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“…Later laboratory and field studies (Barber et al [74], Grenfell [75]) showed that the backscatter of sea ice undergoes a profound evolution as the ice grows and changes. However, because multiple sea ice states can have the same backscatter values (Grenfell et al [76]), backscatter at a single pixel is insufficient to distinguish between ice categories.…”

Section: Analysis Of Sar Data For Sea-ice Classificationmentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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Seasonal and diurnal variations in SAR signatures of landfast sea ice

Cited by 26 publications

References 4 publications

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

Hypertemporal analysis of remotely sensed sea-ice data for climate change studies

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

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